Imagen de Portada

Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bolton, Regan, Sheikhfathollahi, Mohammadreza, Parkinson, Simon, Vulovic, Vanessa, Bamford, Gary, Basher, Dan, Parkinson, Howard
Formato:	Preprint
Publicado:	2025
Materias:	Software Engineering Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: A Railway Casestudy
por: Bolton, Regan, et al.
Publicado: (2025)

FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion
por: Guo, Qi, et al.
Publicado: (2024)

Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models
por: Zhang, Quanjun, et al.
Publicado: (2025)

Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation
por: Gao, Xinyu, et al.
Publicado: (2024)

RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation
por: Wu, Peiyang, et al.
Publicado: (2025)

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models
por: Guo, Hanyang, et al.
Publicado: (2025)

Test code generation at Ericsson using Program Analysis Augmented Fine Tuned LLMs
por: Krishna, Sai, et al.
Publicado: (2025)

From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry
por: Arora, Chetan, et al.
Publicado: (2025)

DRAFT-ing Architectural Design Decisions using LLMs
por: Dhar, Rudra, et al.
Publicado: (2025)

Psychological safety in software workplaces: A systematic literature review
por: Santana, Beatriz, et al.
Publicado: (2025)

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
por: Yanuganti, Vasudha, et al.
Publicado: (2025)

Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation
por: Lu, Hanzhen, et al.
Publicado: (2024)

When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?
por: Chen, Jingyi, et al.
Publicado: (2025)

Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection
por: Trad, Fouad, et al.
Publicado: (2025)

When More Retrieval Hurts: Retrieval-Augmented Code Review Generation
por: Meng, Qianru, et al.
Publicado: (2025)

FGIT: Fault-Guided Fine-Tuning for Code Generation
por: Fan, Lishui, et al.
Publicado: (2025)

Fine-Tuning Models for Automated Code Review Feedback
por: Kumar, Smitha S, et al.
Publicado: (2026)

Retrieval-Augmented Code Review Comment Generation
por: Hong, Hyunsun, et al.
Publicado: (2025)

What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
por: Gu, Wenchao, et al.
Publicado: (2025)

Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning
por: Galliamov, Karim, et al.
Publicado: (2024)

ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement
por: Bhattarai, Manish, et al.
Publicado: (2025)

Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
por: Shin, Jiho, et al.
Publicado: (2023)

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
por: Zhao, Yicong, et al.
Publicado: (2025)

A customizable approach to assess software quality through Multi-Criteria Decision Making
por: Basciani, Francesco, et al.
Publicado: (2023)

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
por: Xu, Xiwei, et al.
Publicado: (2025)

PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
por: Yang, Boyang, et al.
Publicado: (2026)

Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks
por: Haque, Md Zahidul, et al.
Publicado: (2026)

Rethinking the Capability of Fine-Tuned Language Models for Automated Vulnerability Repair
por: Han, Woorim, et al.
Publicado: (2025)

AI-powered software testing tools: A systematic review and empirical assessment of their features and limitations
por: Garousi, Vahid, et al.
Publicado: (2024)

Repairing Responsive Layout Failures Using Retrieval Augmented Generation
por: Zerin, Tasmia, et al.
Publicado: (2025)

AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems
por: Weeger, Nicolas, et al.
Publicado: (2026)

An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
por: Yang, Zezhou, et al.
Publicado: (2025)

ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation
por: Jhaveri, Samyak, et al.
Publicado: (2025)

Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
por: Liu, Shuo, et al.
Publicado: (2024)

A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
por: Zhang, Beiqi, et al.
Publicado: (2024)

Adversarial Robustness of Open-source Text Classification Models and Fine-Tuning Chains
por: Qin, Hao, et al.
Publicado: (2024)

Prompt-based Code Completion via Multi-Retrieval Augmented Generation
por: Tan, Hanzhuo, et al.
Publicado: (2024)

Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
por: Zhang, Quanjun, et al.
Publicado: (2025)

RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
por: Yang, Rui, et al.
Publicado: (2025)

Impact assessment for vulnerabilities in open-source software libraries
por: Plate, Henrik, et al.
Publicado: (2015)